[Under construction — please excuse the mess]
First, let’s get the naming problem out of the way.
What’s is the difference between big data, analytics, and business intelligence, advanced analytics, predictive analytics, enterprise information systems, decision support systems?
Answer: everybody has an opinion, but nobody agrees, and you shouldn’t care.
Industry analysts, experts, vendors, and practitioners have created many different definitions of these terms, and how they relate to each other. The result is so much disagreement that detailed definitions are useless.
Instead: ignore the definitions, focus on the business needs of a particular project, and choose the technology that fits best.
There’s always a new term
After a while, any term associated with analytics starts sounding dated, and people want to come up with a new one. In particular, much tends to be made in the industry of the difference between “backward-looking” reporting and “forward-looking” technologies such as predictive analytics.
But this difference is spurious. As we’ll see in the next chapter, analytics has always been “forward-looking” — what has changed over time is the sophistication of the technology available to do this.
Concentrate on the actual problem at hand
Instead of offering yet another definition of the terms above, this book uses the term analytics as a high-level synonym for all of them.
Why? Because business people typically use them all interchangeably to refer to what they need: better access to the data in order to run the business.
Instead of spending time on industry jargon, executives who care about analytics success should focus everybody on two things: the business goals of the project; and the specific technology to be used.
The business goals for analytics tend to be timeless: greater efficiencies, new opportunities, greater profits and market share.
The technology that’s available, however, is constantly changing and improving. New industry terms are typically created to try to explain the new technologies and why they are different from what came before. Analytics has been around a long time, and so has generated a lot of essentially overlapping terms.
But all these technologies are essentially just different tools, just like spanners, wrenches, and hammers are all useful tools for building a home.
The problem is that many people confuse the technologies available with the business problems to be solved, and end up trying to make home improvements by talking about “hammer problems” or “spanner problems”.
This is a recipe for trouble, because it naturally leads to a focus on the tools rather than the business issue at hand.
Executive leaders have to clearly separate the business goals of any project from the technologies that may be needed to meet those goals.